
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def a__       (			A_,	A_=0.999,	A_="cosine",	):






  '''simple docstring'''





  if alpha_transform_type == "cosine":

    def alpha_bar_fn(A_   ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(A_   ):
      return math.exp(t * -12.0   )

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''   )

  __magic_name__								=			[]
  for i in range(A_   ):
    __magic_name__								=			i / num_diffusion_timesteps
    __magic_name__								=			(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(A_   ) / alpha_bar_fn(A_   ),	A_   )   )
  return torch.tensor(A_,	dtype=torch.floataa   )





class 			UpperCAmelCase_     (					_A    ,			_A	):


  '''simple docstring'''







  a__       			=						[e.name for e in KarrasDiffusionSchedulers]
  a__       			=						2
  @register_to_config
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					int = 1000     ,				UpperCamelCase__     :					float = 0.00085     ,				UpperCamelCase__     :					float = 0.012     ,				UpperCamelCase__     :					str = "linear"     ,				UpperCamelCase__     :					Optional[Union[np.ndarray, List[float]]] = None     ,				UpperCamelCase__     :					str = "epsilon"     ,				UpperCamelCase__     :					str = "linspace"     ,				UpperCamelCase__     :					int = 0     ,				)     ->      List[Any]:







    """simple docstring"""






    if trained_betas is not None:
      __magic_name__								=			torch.tensor(UpperCamelCase__     ,				dtype=torch.floataa       )
    elif beta_schedule == "linear":
      __magic_name__								=			torch.linspace(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				dtype=torch.floataa       )
    elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      __magic_name__								=			(
          torch.linspace(beta_start**0.5     ,				beta_end**0.5     ,				UpperCamelCase__     ,				dtype=torch.floataa       ) ** 2
      )
    elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      __magic_name__								=			betas_for_alpha_bar(UpperCamelCase__       )
    else:
      raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''       )

    __magic_name__								=			1.0 - self.betas
    __magic_name__								=			torch.cumprod(self.alphas     ,				dim=0       )

    #  set all values
    self.set_timesteps(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Tuple=None       )     ->      Optional[int]:







    """simple docstring"""






    if schedule_timesteps is None:
      __magic_name__								=			self.timesteps

    __magic_name__								=			(schedule_timesteps == timestep).nonzero()

    # The sigma index that is taken for the **very** first `step`
    # is always the second index (or the last index if there is only 1)
    # This way we can ensure we don't accidentally skip a sigma in
    # case we start in the middle of the denoising schedule (e.g. for image-to-image)
    if len(self._index_counter       ) == 0:
      __magic_name__								=			1 if len(UpperCamelCase__       ) > 1 else 0
    else:
      __magic_name__								=			timestep.cpu().item() if torch.is_tensor(UpperCamelCase__       ) else timestep
      __magic_name__								=			self._index_counter[timestep_int]

    return indices[pos].item()
  @property
  def 				_lowercase				(					self     :					int       )     ->      Tuple:







    """simple docstring"""






    if self.config.timestep_spacing in ["linspace", "trailing"]:
      return self.sigmas.max()

    return (self.sigmas.max() ** 2 + 1) ** 0.5
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					torch.FloatTensor     ,				UpperCamelCase__     :					Union[float, torch.FloatTensor]     ,				)     ->      torch.FloatTensor:







    """simple docstring"""






    __magic_name__								=			self.index_for_timestep(UpperCamelCase__       )

    if self.state_in_first_order:
      __magic_name__								=			self.sigmas[step_index]
    else:
      __magic_name__								=			self.sigmas_interpol[step_index]

    __magic_name__								=			sample / ((sigma**2 + 1) ** 0.5)
    return sample
  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					Union[str, torch.device] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				)     ->      int:







    """simple docstring"""






    __magic_name__								=			num_inference_steps

    __magic_name__								=			num_train_timesteps or self.config.num_train_timesteps

    # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
    if self.config.timestep_spacing == "linspace":
      __magic_name__								=			np.linspace(0     ,				num_train_timesteps - 1     ,				UpperCamelCase__     ,				dtype=UpperCamelCase__       )[::-1].copy()
    elif self.config.timestep_spacing == "leading":
      __magic_name__								=			num_train_timesteps // self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      __magic_name__								=			(np.arange(0     ,				UpperCamelCase__       ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase__       )
      timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
      __magic_name__								=			num_train_timesteps / self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      __magic_name__								=			(np.arange(UpperCamelCase__     ,				0     ,				-step_ratio       )).round().copy().astype(UpperCamelCase__       )
      timesteps -= 1
    else:
      raise ValueError(
          F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''       )

    __magic_name__								=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    __magic_name__								=			torch.from_numpy(np.log(UpperCamelCase__       )       ).to(UpperCamelCase__       )

    __magic_name__								=			np.interp(UpperCamelCase__     ,				np.arange(0     ,				len(UpperCamelCase__       )       )     ,				UpperCamelCase__       )
    __magic_name__								=			np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
    __magic_name__								=			torch.from_numpy(UpperCamelCase__       ).to(device=UpperCamelCase__       )

    # interpolate sigmas
    __magic_name__								=			sigmas.log().lerp(sigmas.roll(1       ).log()     ,				0.5       ).exp()

    __magic_name__								=			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
    __magic_name__								=			torch.cat(
        [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

    if str(UpperCamelCase__       ).startswith("""mps"""       ):
      # mps does not support float64
      __magic_name__								=			torch.from_numpy(UpperCamelCase__       ).to(UpperCamelCase__     ,				dtype=torch.floataa       )
    else:
      __magic_name__								=			torch.from_numpy(UpperCamelCase__       ).to(UpperCamelCase__       )

    # interpolate timesteps
    __magic_name__								=			self.sigma_to_t(UpperCamelCase__       ).to(UpperCamelCase__     ,				dtype=timesteps.dtype       )
    __magic_name__								=			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])     ,				dim=-1       ).flatten()

    __magic_name__								=			torch.cat([timesteps[:1], interleaved_timesteps]       )

    __magic_name__								=			None

    # for exp beta schedules, such as the one for `pipeline_shap_e.py`
    # we need an index counter
    __magic_name__								=			defaultdict(UpperCamelCase__       )
  def 				_lowercase				(					self     :					Tuple     ,				UpperCamelCase__     :					Dict       )     ->      int:







    """simple docstring"""






    __magic_name__								=			sigma.log()

    # get distribution
    __magic_name__								=			log_sigma - self.log_sigmas[:, None]

    # get sigmas range
    __magic_name__								=			dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
    __magic_name__								=			low_idx + 1

    __magic_name__								=			self.log_sigmas[low_idx]
    __magic_name__								=			self.log_sigmas[high_idx]

    # interpolate sigmas
    __magic_name__								=			(low - log_sigma) / (low - high)
    __magic_name__								=			w.clamp(0     ,				1       )

    # transform interpolation to time range
    __magic_name__								=			(1 - w) * low_idx + w * high_idx
    __magic_name__								=			t.view(sigma.shape       )
    return t
  @property
  def 				_lowercase				(					self     :					Any       )     ->      Dict:







    """simple docstring"""






    return self.sample is None
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					Union[torch.FloatTensor, np.ndarray]     ,				UpperCamelCase__     :					Union[float, torch.FloatTensor]     ,				UpperCamelCase__     :					Union[torch.FloatTensor, np.ndarray]     ,				UpperCamelCase__     :					bool = True     ,				)     ->      Union[SchedulerOutput, Tuple]:







    """simple docstring"""






    __magic_name__								=			self.index_for_timestep(UpperCamelCase__       )

    # advance index counter by 1
    __magic_name__								=			timestep.cpu().item() if torch.is_tensor(UpperCamelCase__       ) else timestep
    self._index_counter[timestep_int] += 1

    if self.state_in_first_order:
      __magic_name__								=			self.sigmas[step_index]
      __magic_name__								=			self.sigmas_interpol[step_index + 1]
      __magic_name__								=			self.sigmas[step_index + 1]
    else:
      # 2nd order / KDPM2's method
      __magic_name__								=			self.sigmas[step_index - 1]
      __magic_name__								=			self.sigmas_interpol[step_index]
      __magic_name__								=			self.sigmas[step_index]

    # currently only gamma=0 is supported. This usually works best anyways.
    # We can support gamma in the future but then need to scale the timestep before
    # passing it to the model which requires a change in API
    __magic_name__								=			0
    __magic_name__								=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
      __magic_name__								=			sigma_hat if self.state_in_first_order else sigma_interpol
      __magic_name__								=			sample - sigma_input * model_output
    elif self.config.prediction_type == "v_prediction":
      __magic_name__								=			sigma_hat if self.state_in_first_order else sigma_interpol
      __magic_name__								=			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
          sample / (sigma_input**2 + 1)
      )
    elif self.config.prediction_type == "sample":
      raise NotImplementedError("""prediction_type not implemented yet: sample"""       )
    else:
      raise ValueError(
          F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''       )

    if self.state_in_first_order:
      # 2. Convert to an ODE derivative for 1st order
      __magic_name__								=			(sample - pred_original_sample) / sigma_hat
      # 3. delta timestep
      __magic_name__								=			sigma_interpol - sigma_hat

      # store for 2nd order step
      __magic_name__								=			sample
    else:
      # DPM-Solver-2
      # 2. Convert to an ODE derivative for 2nd order
      __magic_name__								=			(sample - pred_original_sample) / sigma_interpol

      # 3. delta timestep
      __magic_name__								=			sigma_next - sigma_hat

      __magic_name__								=			self.sample
      __magic_name__								=			None

    __magic_name__								=			sample + derivative * dt

    if not return_dict:
      return (prev_sample,)

    return SchedulerOutput(prev_sample=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					torch.FloatTensor     ,				UpperCamelCase__     :					torch.FloatTensor     ,				UpperCamelCase__     :					torch.FloatTensor     ,				)     ->      torch.FloatTensor:







    """simple docstring"""






    __magic_name__								=			self.sigmas.to(device=original_samples.device     ,				dtype=original_samples.dtype       )
    if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase__       ):
      # mps does not support float64
      __magic_name__								=			self.timesteps.to(original_samples.device     ,				dtype=torch.floataa       )
      __magic_name__								=			timesteps.to(original_samples.device     ,				dtype=torch.floataa       )
    else:
      __magic_name__								=			self.timesteps.to(original_samples.device       )
      __magic_name__								=			timesteps.to(original_samples.device       )

    __magic_name__								=			[self.index_for_timestep(UpperCamelCase__     ,				UpperCamelCase__       ) for t in timesteps]

    __magic_name__								=			sigmas[step_indices].flatten()
    while len(sigma.shape       ) < len(original_samples.shape       ):
      __magic_name__								=			sigma.unsqueeze(-1       )

    __magic_name__								=			original_samples + noise * sigma
    return noisy_samples





  def __len__(					self     :					Union[str, Any]       )     ->      List[str]:







    """simple docstring"""






    return self.config.num_train_timesteps



from __future__ import annotations

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if (direction == 1 and array[indexa] > array[indexa]) or (
      direction == 0 and array[indexa] < array[indexa]
  ):
    __magic_name__	,       __magic_name__								=			array[indexa], array[indexa]

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if length > 1:
    __magic_name__								=			int(length / 2   )
    for i in range(A_,	low + middle   ):
      comp_and_swap(A_,	A_,	i + middle,	A_   )
    bitonic_merge(A_,	A_,	A_,	A_   )
    bitonic_merge(A_,	low + middle,	A_,	A_   )

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if length > 1:
    __magic_name__								=			int(length / 2   )
    bitonic_sort(A_,	A_,	A_,	1   )
    bitonic_sort(A_,	low + middle,	A_,	0   )
    bitonic_merge(A_,	A_,	A_,	A_   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Optional[Any]       			=  input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase     :	List[Any]       			=  [int(item.strip()) for item in user_input.split(',')]

    bitonic_sort(unsorted, 0, len(unsorted), 1)
    print('\nSorted array in ascending order is: ', end='')
    print(*unsorted, sep=', ')

    bitonic_merge(unsorted, 0, len(unsorted), 0)
    print('Sorted array in descending order is: ', end='')
    print(*unsorted, sep=', ')

